Fingerprints of Individual Supermassive Black Hole Binaries in Pulsar Timing Arrays
Chiara M. F. Mingarelli, Bjorn Larsen, Ellis Eisenberg, Qinyuan Zheng, Forrest Hutchison

TL;DR
This paper introduces a new method to identify individual supermassive black hole binaries in pulsar timing array data by detecting their unique spatial correlation pattern, improving localization and distinguishing them from stochastic backgrounds.
Contribution
It derives an analytic expression for the binary's spatial correlation pattern and demonstrates its effectiveness in simulations, enhancing detection and localization capabilities.
Findings
Cross-correlations yield high Bayes factors favoring binary models
Improved sky localization by a factor of 11
Fingerprint-based search is robust against noise over phase-coherent methods
Abstract
With evidence for a nanohertz gravitational-wave background now established by Pulsar Timing Arrays, the search focuses on identifying individual supermassive black hole binaries. We show that these binaries produce a distinct spatial correlation pattern across the array, acting as a deterministic analogue to the stochastic Hellings and Downs curve. We derive a closed analytic expression for this single-source overlap reduction function, , factorizing the signal into a source-dependent amplitude and a purely geometric fingerprint. Using simulated datasets, we demonstrate that this fingerprint breaks the degeneracy between an individual binary and a stochastic background. Including these cross-correlations yields Bayes factors of favoring the continuous-wave model over a stochastic-background model and favoring the continuous-wave model over an…
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Taxonomy
TopicsPulsars and Gravitational Waves Research · Astrophysical Phenomena and Observations · Black Holes and Theoretical Physics
